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19-12-2023 | RESEARCH

Multi-step ahead suspended sediment load modeling using machine learning– multi-model approach

Authors: Gebre Gelete, Vahid Nourani, Hüseyin Gökçekuş, Tagesse Gichamo

Published in: Earth Science Informatics | Issue 1/2024

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Abstract

This study aimed to develop an ensemble machine learning (ML) model for multi-step ahead SSL modeling in the Katar catchment, Ethiopia. To do so, different ML models such as multilinear regression (MLR), Feed-forward Neural Network (FFNN), Support Vector Regression and Adaptive Neuro-Fuzzy Inference System (ANFIS) were applied for one, two and three-step ahead SSL modeling. For this, two years of daily discharge and SSL data were used for model calibration and validation. Finally, four ensemble techniques: neuro-fuzzy ensemble (NFE), neural network ensemble (NE), weighted average ensemble (WE) and simple average ensemble (SE), were developed to improve the performance of single models. The performance of the developed models was evaluated using percent bias (PBIAS), mean absolute error (MAE), root mean square error (RMSE) and Nash Sutcliffe Efficiency Coefficient (NSE). The result shows that ANFIS outperformed the other individual models with a validation phase NSE value of 0.916,0.9 and 0.88 and RMSE value of 1630.5 ton/day, 1850.6 ton/day and 2026.6 ton/day, for one, two and three steps-ahead predictions, respectively. The NFE technique improved the individual model’s performance in the validation phase up to 42.17%, 49.84% and 60.66% for one, two and three-step ahead modeling. Generally, the use of ensemble techniques resulted in promising improvements in single and multi-step ahead SSL modeling.

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Title: Multi-step ahead suspended sediment load modeling using machine learning– multi-model approach
Authors: Gebre Gelete
Vahid Nourani
Hüseyin Gökçekuş
Tagesse Gichamo
Publication date: 19-12-2023
Publisher: Springer Berlin Heidelberg
Published in: Earth Science Informatics / Issue 1/2024
Print ISSN: 1865-0473
Electronic ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-023-01192-4

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